A Lidar-Based Decision-Making Method for Road Boundary Detection Using Multiple Kalman Filters

In this paper, a novel decision-making method is proposed for autonomous mobile robot navigation in an urban area where global positioning system (GPS) measurements are unreliable. The proposed method uses lidar measurements of the road's surface to detect road boundaries. An interacting multiple model method is proposed to determine the existence of a curb based on a probability threshold and to accurately estimate the roadside curb position. The decision outcome is used to determine the source of references suitable for reliable and seamless navigation. The performance of the decision-making algorithm is verified through extensive experiments with a mobile robot autonomously navigating through campus roads with several intersections and unreliable GPS measurements. Our experimental results demonstrate the reliability and good tracking performance of the proposed algorithm for autonomous urban navigation.